Backport PR #1077: BUG: Allow 1d exog in ARMAX forecasting.

Closes #1076. Another candidate for backporting.

statsmodels/tsa/arima_model.py

@@ -120,7 +120,10 @@
             want out of sample prediction.
         exog : array-like, optional
             If the model is an ARMAX and out-of-sample forecasting is
-            requested, exog must be given.
+            requested, exog must be given. Note that you'll need to pass
+            `k_ar` additional lags for any exogenous variables. E.g., if you
+            fit an ARMAX(2, q) model and want to predict 5 steps, you need 7
+            observations to do this.
         dynamic : bool, optional
             The `dynamic` keyword affects in-sample prediction. If dynamic
             is False, then the in-sample lagged values are used for
@@ -203,6 +206,15 @@ def _get_predict_out_of_sample(endog, p, q, k_trend, k_exog, start, errors,
         if k_exog > 0:
             #TODO: technically should only hold for MLE not
             # conditional model. See #274.
+            # ensure 2-d for conformability
+            if np.ndim(exog) == 1 and k_exog == 1:
+                # have a 1d series of observations -> 2d
+                exog = exog[:, None]
+            elif np.ndim(exog) == 1:
+                # should have a 1d row of exog -> 2d
+                if len(exog) != k_exog:
+                    raise ValueError("1d exog given and len(exog) != k_exog")
+                exog = exog[None, :]
             X = lagmat(np.dot(exog, exparams), p, original='in', trim='both')
             mu = trendparam * (1 - arparams.sum())
             # arparams were reversed in unpack for ease later
@@ -1337,7 +1349,10 @@ def forecast(self, steps=1, exog=None, alpha=.05):
         exog : array
             If the model is an ARMAX, you must provide out of sample
             values for the exogenous variables. This should not include
-            the constant.
+            the constant. Note that you'll need to pass `k_ar` additional
+            lags for any exogenous variables. E.g., if you fit an ARMAX(2, q)
+            model and want to predict 5 steps, you need 7 observations
+            to do this.
         alpha : float
             The confidence intervals for the forecasts are (1 - alpha) %
 

statsmodels/tsa/tests/test_arima.py

@@ -1822,6 +1822,15 @@ def test_arima_dataframe_integer_name():
     df = pandas.DataFrame(ts)
     mod = sm.tsa.ARIMA(df, (2, 0, 2))
 
+def test_arima_exog_predict_1d():
+    # test 1067
+    np.random.seed(12345)
+    y = np.random.random(100)
+    x = np.random.random(100)
+    mod = ARMA(y, (2, 1), x).fit(disp=-1)
+    newx = np.random.random(12)
+    results = mod.forecast(steps=10, alpha=0.05, exog=newx)
+
 if __name__ == "__main__":
     import nose
     nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb'], exit=False)